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GPGPU

Yesterday we talked about how the module that interacts with users can be constructed, today I’ll be talking about something more meaty and substantial, I call it the Conduit Module.

To accept open-ended query’s that transcend the simplistic search term based algorithms that Google and others employ we must shift towards a completely different, somewhat unused territory of programming, a conventional programmer’s worst nightmare, the self-modifying program.

Most programmers shy away from self-modifying code as the concept is generally only useful for a few specialized applications such as encryption/obfuscation and viruses and has the potentially disastrous potentiality of being unable to read/alter your own source code, in Jarvis’ case, self modifying code is required and will not be manipulated by humans once trained.

To teach a hierarchical neural network to learn how to program answer sets and the standard functional pieces that make-up a program (IE: for loops with counters, sorting algorithms, etc), we need to teach the network the basics of programming syntax (boundary conditions and limits of manipulation) and then train it by giving it a large number of interesting and unique challenges. In the end we will have a trained neural network that understands not only how to complete a certain task that’s requested of it, but also taking information that is output from the language module as an input telling it what it’s next task could be able to program its way into a solution for a large variety of problems, hence open-ended.

A process like this is complicated to imagine let alone create, however such a module also has the capability of altering the processing of other parts of Jarvis adapting them to perform better over time via testing and training, enabling another layer to be built on top of the language/conduit modules that enable behaviour and quasi-conciousness.

As you can imagine from a neural network based program as complicated as this, super computers will need to be used and storage of “genes” will need to placed in very large data storage locations. Tomorrow I will be discussing the centralized training and decentralized ownership model and how we can leverage decentralized currencies (bitcoins, altcoins, etc) to help perform the calculations Jarvis will need to be able to learn in a reasonable amount of time.

An artificial intelligence created from Neural Networks must be multi layered; This kind of structure is considered a “Hierarchical Neural Network” and is required as the information from lower level functions are necessary to feed into the higher level abstract functions, exactly how the human brain operates.

Any AI that plans on interfacing with humans will need to have one fundamental module, a language interpreter.

Starting from the beginning we start to see multiple layers of neural networks that feed into each other required to complete open ended query/solution tasks that Jarvis will need to be adept at solving. Word parsing is by far the easiest of all steps, as it is a simple English (or equivalent) word parsing module that is trained via dictionary/thesaurus and by reading the responses to strings on webpages (forums are a great resource for natural language datasets) and by training the network using data mined from these resources we can program a network that fully understand not only words and grammar but entire sentence structures and query/answers in the natural language of conversation.

Once the language is interpreted and the program understands what you’re asking right now, it may go back and look into previous queries to uncover patterns in your questions. An example would be if you were looking for a waterproofing device to fit around a cable, and your previous question was about silicone sealants, Jarvis may ask in response “Are you looking for other waterproofing techniques as well?” and by going back and forth multiple times you will end up with a solution you didn’t even know you were asking.

Tomorrow I will talk about how such an open-ended solver could not only be used for data retrieval, but also used to actually act upon something, eventually I will be able to say “Jarvis, please install yourself onto my USB drive” and in response Jarvis will say “Sure James, do you need me to call the movers? They seem to be running an hour late.”

Sorry everyone for the lack of updates, I was able to find work for a few months as a Product Design Engineer unfortunately it looks like the company might not be financially stable enough to pay me regularly which as a silver lining gives me more time to work on my ANN projects.

For the next couple of weeks I will be describing a major project that my colleague @Brandon.Benvie and I have been working on and keeping under our wings in preparation of working on it full time, as it has the possibility to completely revolutionize how the internet the likes of which have never been seen before in history.

This ANN project, named Jarvis will be a fully functional companion AI that will have its computational and storage requirements distributed inside of a digital currency similar to the Bitcoin Protocol using some significant tweaks to enable custom kernel code computations.

I’ve also added some light reading for anyone interested in freshening up before we get into the technical details of exactly how such a system will be constructed, I promise this will be a hell of a ride.

What is the definition of Machine learning? The wikipedia definition of the term is as follows:

machine learning

Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.

What does that mean? How does it work? this article is going to go over both of these fundamental questions and talk specifically about Artificial Neural Networks (ANNs) and why they are such a powerful tool in the hands of the savvy developer.

Imagine you have a “smart key” lock on your door, these locking mechanisms have a special feature that allows them to reset the tumblers and form fit to the teeth of a new key. This new key is now the only key that will open the lock, as the lock now “knows” what key is valid and what isn’t, and it was taught to acknowledge the current key as the only valid key, and a key with even a slight variation of teeth would be considered invalid.

What I just described was a layman’s view of what a neural network does, and how machine learning works. a basic Artificial Neural Network functions by taking a set of input parameters (they could be any variable that could even remotely impact the output value), putting them through a series of plastic ( malleable) coefficients, which are then plugged into an activation function which spits out what it thinks to be an output value.

Y(O1, O2) = A(1*C1*A(1*C2+I1*C3+I2*C4+….+ In*Cn+2))

The power of this system comes in the randomization of the weight choices. Weights are initially randomized and then by a multitude of potential methods are refined to produce less and less error per iteration (The specific reduction methods will be discussed in further detail in another talk.)

As the system reduces error iteratively, it will eventually reach a point where the error between the expected result of the dataset and the real value are nearly indistinguishable, however the network is not yet verified.

The next step is to plug the weight values into a verification network (IE the weights are fixed, and the input and ouput values iterate through the verification data). If the network was sufficiently trained, then it will pass the verification stage and the designer will able to acknowledge the strengths and limitations of this particular net, and use that to forecast with reasonable certainty of its results.

A network like I just described could be as varied as Google’s house number optical character recognition software (http://phys.org/news/2014-01-google-team-neural-network-approach.html), a “suspicious person” face recognition software for law enforcement and transit authorities, or a smart commodities and stock trader that is able to see past speculation and find the true value of a particular commodity, and communicate with other smart traders to determine the direction of the global economy.

Now there are drawbacks of course; The computational power required to do any of these individual tasks is significant, and the training dataset must be sufficiently large, varied and transparent enough so that the system can learn the intricacies and fundamentals of how each system obeys underlying natural laws.

However with the advent of “big data”, obtaining such information is becoming quite straightforward albiet sometimes expensive, and with more powerful computing methods such as GPGPU (general purpose graphics processing unit) computing via openCL, the power of neural networks and smart agents is just starting to be realized.